Canadian Health Personnel Attitudes Toward Refugee Claimants’ Entitlement to Health Care
Bibliographic record
Abstract
Health care personnel attitudes toward refugee claimant entitlement to health care are influenced by multilevel factors including institutional and societal culture. Although individual attitudes may be modified through training, macro- and meso-issues require system-level interventions. This paper analyzes the role of individual-, institutional-, and city-level factors in shaping attitudes toward refugee claimants' access to health care among Canadian health care personnel. A total of 4207 health care personnel in 16 institutions located in Montreal and Toronto completed an online survey on attitudes regarding health care access for refugee claimants. We used multilevel logistic regression analysis to identify individual-, institutional-, and city-level predictors of endorsing access to care. Participants who had prior contact with refugee claimants had greater odds of endorsing access to care than those who did not (OR 1.13; 95% CI 1.05, 1.21). Attitudes varied with occupation: social workers had the highest probability of endorsing equal access to health care (.83; 95% CI .77, .89) followed by physicians (.77; 95% CI .71, .82). An estimated 7.97% of the individual variation in endorsement of equal access to health care was attributable to differences between institutions, but this association was no longer statistically significant after adjusting for city residence. Results indicate that the contexts in which health care professionals live and work are important when understanding opinions on access to health care for vulnerable populations. They suggest that institutional interventions promoting a collective mission to care for vulnerable populations may improve access to health care for precarious status migrants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".